R4DS ch 28 Exercises

28.2.1 Exercises

1. Create one plot on the fuel economy data with customised title, subtitle, caption, x, y, and colour labels.

ggplot(mpg) +
  geom_jitter(aes(hwy, cyl, color = class), width = 0.2, height = 0.2) +
  geom_smooth(aes(hwy, cyl), se = FALSE) + 
  labs(
        title = "Fewer cylinders leads to better fuel economy",
    subtitle = "4-cylinder vehicles are most numerous and thus widely distributed",
    caption = "Data from fueleconomy.gov",
    x = "Highway fuel economy (mpg)",
    y = "number of cylinders",
    color = "Car type"
  )

2. The geom_smooth() is somewhat misleading because the hwy for large engines is skewed upwards due to the inclusion of lightweight sports cars with big engines. Use your modelling tools to fit and display a better model.

mpg2 <- filter(mpg, class != "2seater")
# mpg2 <- mpg # can be used to check that line of fit changes

ggplot(mpg, aes(displ,hwy)) +
  geom_jitter(aes(color=class), width=.1, height=.1) +
  geom_smooth(data = mpg2) +
  labs(
    title = "Fuel efficiency generally decreases with engine size",
    subtitle = "Two-seaters omitted for line fitting because of their light weight",
    caption = "Data from fueleconomy.gov"
  )

3. Take an exploratory graphic that you’ve created in the last month, and add informative titles to make it easier for others to understand.

require(forcats)
rincome_simple <- gss_cat

rincome_simple$rincome <- fct_lump(gss_cat$rincome) %>%
  fct_collapse(
    "<5000" = c("Lt $1000", "$1000 to 2999", "$3000 to 3999", "$4000 to 4999"),
    "5000-14999" = c("$5000 to 5999", "$6000 to 6999", "$7000 to 7999", "$8000 to 9999", "$10000 - 14999"),
    "15000+" = c("$15000 - 19999", "$20000 - 24999", "$25000 or more")
  )

filter(rincome_simple, rincome == "<5000" | rincome == "5000-14999" | rincome == "15000+") %>%
  ggplot()+
  geom_bar(aes(rincome)) +
  labs(
    title = "Most respondents reported incomes greater than $15k",
    x = "Reported income",
    y = "Number of respondents"
  )

28.3.1 Exercises

1. Use geom_text() with infinite positions to place text at the four corners of the plot.

label_tr <- tibble(
  displ = Inf,
  hwy = Inf)

label_tl <- tibble(
  displ = -Inf,
  hwy = Inf)

label_br <- tibble(
  displ = Inf,
  hwy = -Inf)

label_bl <- tibble(
  displ = -Inf,
  hwy = -Inf)

ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  geom_text(aes(label="top right"), data=label_tr, vjust="top", hjust="right") + geom_text(aes(label="top left"), data=label_tl, vjust="top", hjust="left") + geom_text(aes(label="bottom right"), data=label_br, vjust="bottom", hjust="right") + geom_text(aes(label="bottom left"), data=label_bl, vjust="bottom", hjust="left")

2. Read the documentation for annotate(). How can you use it to add a text label to a plot without having to create a tibble?

Takes arguments x = and y = so you can specify individual sets of coordinates quickly.

p <- ggplot(mtcars, aes(x = wt, y = mpg)) + geom_point()
p + annotate("text", x = 4, y = 25, label = "Some text")

3. How do labels with geom_text() interact with faceting? How can you add a label to a single facet? How can you put a different label in each facet? (Hint: think about the underlying data.)

   Labels are applied to every facet by default.

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point() + 
  geom_text(aes(label = model)) +
  facet_wrap(~ class, nrow = 2) 

You can set the data for geom_text to a new custom data source that contains a the value(s) of the faceting variable for which you want to add a label.

label_df <- tibble(
  displ = Inf,
  hwy = Inf,
  class = "compact",
  label = "Compacts demonstrate more dramatic efficiency \ngradients, perhaps due to extremely efficient outliers."
)

ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  geom_text(aes(label = label), data = label_df, size = 1.5, vjust = "top", hjust = "right") +
  geom_smooth(se = F, method = "lm") +
  facet_wrap( ~ class)

4. What arguments to geom_label() control the appearance of the background box?

label.padding, label.r, label.size, also alpha & color via aesthetics.

5. What are the four arguments to arrow()? How do they work? Create a series of plots that demonstrate the most important options.

   angle, length, ends, type. Angle takes degrees to determine the fatness of the arrowhead. length = unit(value, “unit”). Ends takes first, last, or both to determine which end gets an arrowhead (default is last). type gets open or closed to display a filled or hollow arrowhead (default is open).

df <- tibble(a = (1:5),
             b = (6:10),
             c = (11:15))

arw1 <- arrow(angle = 40, length = unit(50, "bigpts"))
arw2 <- arrow(angle = 20, length = unit(10, "bigpts"))
arw3 <- arrow(angle = 40, length = unit(20, "bigpts"), type = "closed")

qplot(data = df, x = a, y = b) +
  geom_segment(x = 2, y = 7, xend = 4, yend = 9, arrow = arw1)

qplot(data = df, x = a, y = b) +
  geom_segment(x = 2, y = 7, xend = 4, yend = 9, arrow = arw2)

qplot(data = df, x = a, y = b) +
  geom_segment(x = 2, y = 7, xend = 4, yend = 9, arrow = arw3)

28.4.4 Exercises

1. Why doesn’t the following code override the default scale?

   df <- tibble(
     x = rnorm(10000),
     y = rnorm(10000)
   )
   
   ggplot(df, aes(x, y)) +
     geom_hex() +
     scale_colour_gradient(low = "white", high = "red") +
     coord_fixed()

   Color scale isn’t overwritten because the appropriate function would be scale_fill_gradient(). The coordinate scale, however, is overwritten by coord_fixed().

2. What is the first argument to every scale? How does it compare to labs()?

The first argument for every scale is name. Does the same thing as xlab(), for example.

3. Change the display of the presidential terms by:

   1. Combining the two variants shown above.
   2. Improving the display of the y axis.
   3. Labelling each term with the name of the president.
   4. Adding informative plot labels.
   5. Placing breaks every 4 years (this is trickier than it seems!).

presidential2 <- presidential %>%
  mutate(id = as.integer(33 + row_number())) # Create president number

# Create column w/ ordinal rank of prez # & name
presidential2 <- presidential2 %>% 
mutate(presidents = paste0(as.character(name), ", ", sapply(id, toOrdinal::toOrdinal)),
       presidents = factor(presidents, levels = presidents))

# Set up breaks variable for x axis (every 4 years)
year_var <- make_date(seq(year(presidential$start[1]),year(presidential$start[length(presidential$start[])])+8, by = 4))

#plot it
ggplot(presidential2, aes(start, presidents, color = party)) +
    geom_point() +
    geom_segment(aes(xend = end, yend = presidents)) +
    scale_colour_manual(values = c(Republican = "red", Democratic = "blue")) +
    scale_y_discrete() + # labels = c(as.character(presidents))
      scale_x_date(
        breaks = year_var,
        date_labels = "'%y"
        ) +
  labs(title = "Republicans dominate presidencies 1953–present",
       x = "Year",
       y = "President, nth")

4. Use override.aes to make the legend on the following plot easier to see.

   ggplot(diamonds, aes(carat, price)) +
     geom_point(aes(colour = cut), alpha = 1/20) +
     guides(color = guide_legend(override.aes = list(alpha = 1))) # this works! right in the documentation examples, too.